Search

Present paper focuses on the development of a planning model for upgradation of rural roads keeping Pradhan Mantry Gram Sadak Yoyna (PMGSY) program as the base of this model. Database for the PMGSY roads of Gandevi block of Navasari district in Gujarat state is prepared which includes data like village data, road network & traffic data. Core network is prepared for all the villages under study which can be defined as rural network required for providing ‘basic accesses to all villages. i.e. all villages are connected nearby market centre and essential places from all weather roads. Planning model is classified in two phases. First phase is the network planning and the second phase is upgradation of roads. For the network planning utility values of the villages is calculated by using Delphi technique and on the basis of that alternative routes are decided. For the upgradation of existing roads, Pavement Condition Index (PCI) value of each road is determined and based on these values upgradation of roads are priorities. The rural road network planning methodology for PMGSY roads based on the accessibility concept is presented in this paper and implemented using Geographic Information System (GIS) technology.

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R2) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency

This research paper investigates urban sprawl in Thiruvananthapuram Urban Agglomeration (UA) and attempts to delineate Urban Growth Boundary (UGB) for promoting urban sustenance. A 112% rise in the spatial expanse of Thiruvananthapuram UA from 256.22 km2 in 2001 to 542.57 km2 in 2011 might induce urban sprawl in the peripheral areas. The Landsat satellite imagery for the years 1987, 1997, 2007, and 2017 were extracted to examine the spatiotemporal urban growth pattern. Shannon’s entropy index was employed to detect urban sprawl in Thiruvananthapuram UA. The UGB delineation process involved future urban growth prediction using the MOLUSCE (Modules for Land Use Change Simulations) plug-in of QGIS software. ANN-MLP (Artificial Neural Network-Multi Layer Perceptron) and CA (Cellular Automata) model was preferred in MOLUSCE to predict future urban growth for the year 2027. Thereafter, hexagons of one square kilometer were used to demarcate the Contiguous built-up Growth Boundary (CGB), and later, sub-administrative units were selected to delineate UGB. The results revealed a rise in the built-up areas from 36.04 km2 in 1987 to 140.69 km2 in 2017. Shannon’s entropy index indicated the prevalence of urban sprawl in Thiruvananthapuram UA. The future growth prediction by 2027 exhibited a further rise in built-up areas to 173.31 km2. The total area within CGB is 213.58 km2, while UGB accounted for 355.59 km2, which included 16 sub-administrative units. This study exhibited a unique methodology to delineate the urban growth boundary, which optimizes the future land requirements in developing nations.

With the advent of multispectral scanners and the availability of digital data, information extraction through remote sensing has become one of the viable tools for studying natural resources. Normally thick vegetation and soil cover are common obstacles while geologically studying an area remotely. The study area, Goa, is largely covered by settlements, private mines, and dense vegetation. This makes it difficult to decipher lithology, structures and to find their extension by ground surveying. In this paper, an attempt has been made to study a variety of image enhancement and analysis techniques to delineate geological features, lineaments, and several landuse features. The information gathered from land use features and vegetation cover is also utilized in delineating lithology and lineaments. Landsat Multi-Spectral Scanner (MSS) data both in the visual and digital form have been used for the analysis. Various photographic techniques such as Bas-relief, combined printing of positive and negative for different bands, color composites, and digital image processing techniques like ratioing, principal component analysis and ratioing of the first two principal components have been applied for geological information extraction. This paper examines comparative utility of enhancement techniques in studying geological aspects. It is found that the ratio image of PCI and PC2 gives most significant and detailed information with maximum contrast and sharp boundaries. Bas-relief images are excellent for identifying geomorphic features and lineaments.

Slum areas are dense urban areas in which the building size is quite small, and the buildings are interconnected with each other. Also, there is a lot of variation in the texture of slum area buildings, which makes the extraction of individual buildings in slum areas a tough task. In this paper, a methodology has been proposed which aims to extract slum built-up areas using multispectral satellite images using MATLAB software. In the proposed methodology, two building masks have been prepared from the input image by using threshold value and Laws’ texture energy measure. After that, another building mask has been prepared by using these two masks and vegetation, non-building areas and shadow areas have been removed from it, which finally results in the detection of slum built-up areas. The proposed methodology has been applied on three subsets of QuickBird satellite image containing slum built-up areas. For accuracy assessment, the total slum area extracted from the proposed methodology has been compared with the total area obtained by manually digitized buildings. The overall accuracy of slum built-up extraction with respect to area has been found to be more than 83%. Due to resemblance of building and road texture, some over-extraction of road as slum built-up areas has also been observed in only one subset image. No over-detection has been found in other two subset images.

Basin-wise DEM-based Automated Morphometry (BaDAM) toolbox, an automated solution for drainage basin morphometric analysis, is presented in this paper. The toolbox, being scripted in Python, can be easily integrated with ArcGIS 10.0 version, and customizations can be implemented effortlessly. The BaDAM toolbox has been tested for computation of morphometric attributes of a tropical river basin in Kerala (India) using SRTM DEM (30 m), and the analysis was carried out successfully with less time complexity and with significant efficiency. Further, the results of the DEM-based morphometric analysis generated by the toolbox showed good agreement with the results of a previous study using SRTM data for the same river basin. It is suggested that the toolbox, which takes care of most of the inherent limitations of the existing programs/tools, can be used for DEM-based basin morphometric analysis for generating comparatively quicker and accurate results.

This paper gives an overview of a major application of modern geospatial tools such as remote sensing, GIS and GPS, i.e., 3S technology in estimating travel demand along Indian roads by considering the study area, Tiruchirappalli urban city in Tamil Nadu. In the study, an attempt was made to estimate travel demand based on the current land-use classification as the pattern of travel depends on the type of land use activity in a zone. IRS high resolution image Cartosat-1 of year 2009 was used to extract the land-use information required. The travel demand model developed was validated with the field obtained OD-matrix to understand the accuracy of the method considered.

The growing population and the need for improving the living conditions of Egyptian community have resulted in the establishment of new urban areas, industrial zones, land reclamation, and different types of developmental projects. In the Nile Valley, the only available areas for carrying out such activities are the desert fringes surrounding the floodplain on both sides of the Nile Valley. This study examines the use of image processing mapping to landuse and land cover changes for the desert fringes along the Nile Valley between Assiut-Sohag area between 1984 and 2013 (satellite images). The layers of landuse map were digitized by heads-up digitization method in GIS environment. Similarly the layers of landuse map (2013) were generated by supervised classification of satellite imagery. The training site was created by referring landsat7 ETM+ and Landsat 8 satellite imagery with help of GPS (Global Positioning System) coordinates in GIS environment. Supervised classification technique was adopted to classify the satellite image in GIS (Geographic Information System) software environment. The classified image was converted into vector format and estimated the total area of each class by using geometry tools of GIS software. The landuse changes between 1984, 2000 and 2013 were compared and displayed in geographical or map format in 1:100000 scale. Also, this paper describes a study that was carried out to perform supervised and unsupervised techniques on remote sensing data for land cover classification and to evaluate the accuracy result of both classification techniques. The land cover classes for the study area were classified into 5 themes. Ground verification was carried out to check and assess the accuracy of classification based on Google Earth pro. A total of 72 sample points were collected using Systematic Random Sampling. The results showed that the overall accuracy for the supervised classification was 95.8 % where Kappa statistics was 0.78.

Analysis of urban sprawl is an issue that has been continuously attracting attention in the planning and research community. Τhis paper presents the results of an analysis of the growth of the city of Rethymno during the 1997–2010 time period. Rethymno is a city in the island of Crete in Greece with population of about 35,000 people, in which developed land has expanded at a rate that is double the growth of the population during the study period. A qualitative analysis was first performed to identify growth patterns in the different parts of the city, how these are related to planning regulations and the extent of cohesiveness of the development. A logistic regression model was estimated using various variables influencing the expansion of the built up area. Variables such as slope, distance from main roads, distance from the University, distance from coastline, as well as variables describing the proximity to other developed areas were used as independent variables in the logistic regressions. Planning constraints with respect zoning were also considered. The accuracy/goodness of fit of the simulation results were also tested using Receiver Operating Characteristic (ROC) curve. The results revealed high (performance) accuracy, which can support the applicability of the proposed method in urban sprawl modeling. Once the equations were estimated they were applied using data from 2010 to identify future trends of urbanization. The methodology adopted in this study can result in a tool that can be of use to urban planning authorities in identifying areas of future urban growth and therefore, adopt zoning policies encouraging or discouraging growth in these areas according to the sustainability objectives of the local community.

Satellite remote sensing images play an important role in environmental monitoring for mining industry. There are a number of environmental variables, soil and surface variables, associated with mineral activities, that are to some extent detectable easily with satellite earth observation data. The aim of this paper is to detect the quarrying-mining activities are located in seismically active regions of Turkey using satellite images. Because mining-quarrying blasts have been observed and listed as earthquakes in the seismicity catalogue by seismic networks. The presence of mines-quarries in an active seismic zone can cause errors during the analysis of the distribution of microseismicity and the editing of seismic catalogs. Therefore, this study is a meaningful analysis for seismic networks interested in tectonic researches, because it highlights areas where need to pay careful attention is advisable for identification of mining-quarrying blasts. The new digital database was created using the satellite images of mining and quarrying areas taken from the Google Earth program ( http://maps.google.com ). In the study, approximately 721 known and illegal mining-quarrying sources have been detected. That were organized in an informational atlas includes information on locations, geographic coordinates and satellite images of the mining-quarrying sources in Turkey which can be distributed as a CD or on the WEB. Kekovali et al. (International Journal of the Physical Sciences 6(15), 3784–3794, 2011) estimated potential mining and quarry areas of Turkey from the Kandilli Observatory Earthquake Research Institute & National Earthquake Monitoring Center (KOERI-NEMC) seismic catalogs using daytime to nighttime ratio analysis (Qm). In this study, the correlation between the estimated satellite locations of the mining and quarrying activities and the areas with Qm ≥ 2.0 values taken from the previous study was examinated. The result of the study, the important potential mining-quarrying sources were estimated for Turkey includes: Kutahya, Mugla, Manisa, Balikesir, Istanbul, Izmit, Edirne, Bursa, Bilecik, Tekirdag, Ankara, Konya, Eskisehir, Malatya, Yozgat, Kirikkale, Malatya, Sanliurfa, Sinop, Trabzon. Monitoring and controlling mining-quarrying activities through traditional methods is quite difficult due to high costs and lengthy time in obtaining accurate and updated maps. The use of satellite images is an inexpensive and effective tool for mapping large mining-quarrying activity areas that can be also used to supplement data from environmental studies. In the future work, the satellite database can be processed and analyzed in order to produce a proper GIS database that includes important mining-quarrying sources of Turkey.

The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns.

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.

Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis.

In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model

The coastal zones around the world are very densely populated and hence heavily packed with related infrastructures. So, the territorial nations have obvious apprehensions against the IPCC SRES (Intergovernmental Panel on Climate Change, Special Report on Emission Scenario) predicted sea level rise, as it would cause flooding of the low lying coasts and also other related chains of environmental endangers. This has driven these nations to initiate research studies in multiple directions for scientifically evaluating the phenomenon and impacts of sea level rise using all possible technologies including the Geomatics which possesses unique credentials in geosystem mapping. But certain advanced virtues available with Geomatics technology are yet to be capitalized deservingly in this. In addition, almost all the earlier studies have focused only on the impacts of sea level rise (SLR) and not on the predicted shift of high tide line (HTL) and the related inter tidal activities, which would cause a series of environmental disaster. Hence, the present research study was undertaken in a test site of 750 km2 in central Tamil Nadu coast to visualize the areas prone to submergence due to predicted SLR and areas prone to environmental disasters/degradation viz. erosion, deposition, salination of agricultural lands, pollution of aquifers, etc. due to predicted shift of HTL, using digital elevation models derived from SRTM data (Shuttle Radar Topographic Mission), geomorphology and land use/cover maps interpreted using IRS P6 LISS IV satellite data. The paper narrates the certain newer concepts and methodologies adopted in the study and the results.

This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).

This paper is the first ever attempt to study population distribution in Al Ain city in Eastern United Arab Emirates (UAE) through integration of remote sensing and Geographic Information System (GIS). The remote sensing data used in this study included high spatial resolution (1 m) IKONOS imagery of February 17, 2001. For the population related studies IKONOS data offers number of advantages over other satellite images, e.g. it has high spatial resolution, it covers a larger area per image, it cost less per km2, and available on a more regular basis. Such characteristics provide a mechanism by which population estimates can be updated with high accuracy and better rate of frequency. The average difference between the population recorded in the 2001 and that estimated from IKONOS images for Al Ain city is found to be equal to 5%.

Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore - Mysore highway. Various GIS base layers such as builtup areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon's entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon's entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.

The remote sensing data combined with Geographical Information System (GIS) technique has been proved to be very efficient in identification of groundwater potential of any area. In the present paper, IRS 1 A, LISS II data has been used to identify the groundwater potential zones by integrating various thematic maps generated on 1:50,000 scale. These maps are integrated after assigning weight factors to the identified features in each thematic map depending upon their infiltration capacities and the groundwater potential zones in Bhamini mandai (developmental block) of Srikakulam district, Andhra Pradesh are demarcated. The area of investigation has been classified into seven groundwater potential zones. The present results show that integration of all attributes provides more accurate results in groundwater potential zones identification.

Considering the fest pace of development, up-to-date maps have become imperative in developmental planning. Conventional map updating techniques are expensive and time consuming. The present paper makes an attempt to tackle the problem using GIS techniques. Maps generated from IRS-1A LISS II data and Survey of India (SOI) toposheets were used as input maps in GIS. These maps were overlaid to obtain the positional errors at road junction points. The area statistics of each sector were calculated taking the SOI map as reference data. The results show that the areas calculated from LISS II data and SOI map are well comparable and the deviations are within 10%, whereas the positional accuracy (MSE) of points is within 30 m. The study demonstrates that the IRS-1A LISS II data can be used for updating of maps on scale 1:100,000 and smaller in areas where linear features are identifiable in the IRS LISS II image.

The paper describes the details of a comparative study of geological interpretations carried out from Synthetic Aperture Radar (SAR) imagery, Landsat MSS (B & W) imagery and Aerial Photographs, covering 2100 sq km of area in Anantapur district of Andhra Pradesh. The area comprises Peninsular—Gneissic Complex and rocks of Dharwar and Cuddapah Super Groups beside the Quaternary alluvial deposits along the Penneru river and its tributaries. Geomorphologically the areas is represented by denudational, fluvial and structural landforms. The study indicates that the details of the geological and geomorphological maps prepared from SAR imagery and aerial photographs are comparable despite the smaller scale of SAR imagery while the same are not exhibited in Landsat imagery mainly due to its low resolution. Although broad lithological units are possible to be discriminated on SAR as well as aerial photographs, some of the finer rock types viz. gabbroic dykes could be discriminated from the delerite dykes in the SAR imagery due to their different surface roughness. Stereoscopic coverage and enhanced micro-relief of SAR imagery gives better geomorphological details in comparison to aerial photographs. A detailed study of lineaments has also been carried out which shows that in SAR imagery there is over-representation of short lineaments due to enhanced micro-relief and radarshadow effects across the look direction and under-representation of lineaments along the look direction. Landsat imagery is perhaps the best for demarcating lineaments of regional magnitude while aerial photographs are good for depicting shorter lineaments. However, certain lineaments seen in SAR imagery are often not continuously seen on aerial photographs.

With the launching of LANDSAT—1 in 1972 followed by LAND-SAT-2 and 3 in 1975 and 1978 respectively, a new tool is now available to earth scientists for a synoptic overview of the earth forms. Existing small scale geological maps, soil maps and forest resource maps are constantly being updated with the help of LANDSAT imagery. The paper highlights the utility of LANDSAT imagery in preparing small scale geomorphological maps by citing examples from such diversified terrains as the Himalaya and rain forest area in the north and NE India and part of east coast. The advantage of conjunctive use of space craft, aircraft and ground data in enhancing the detail and quality of the map is discussed. Further the direct relationship between landform and landuse is brought out in the map.

This paper mainly projects the attempts made in analysing the computer line printer outputs of bands 4,5 and 7 obtained from Landsat digital data, with reference to a selected area of Chandrapur project in Chandrapur district, Maharashtra. The distinction into various landuse patterns has been carried out on the printed outputs and they have been compared with the available toposheets ane aerial photos.

The Paper describes an effort made in mapping the existing Land Use in the City Centre of Dehra Dun, using Aerial Photo-interpretation techniques. An attempt is also made towards evolving a suitable land use classification for an Indian Urban situation as well as to test the land use classification scheme designed by the Town and Country Planning Organization (TCPO), Delhi. Black and white aerial photographs on a scale of 1:11,000 (approximately) taken by a wide angle lens camera were used. Field verification of random samples was carried out to check the validity of classification.

In this paper house density has been calculated on medium scale photographs and efforts have been made to recognize the physical characteristics of houses on aerial photographs for the purpose of house density mapping. Although the aerial photographs reveal wealth of information regarding the houses, only the residential plots as house units have been taken into account for the present study.

The rough terrain between the Gogar Dhar (Mandi-Brot rise) and the Kullu-Larji valley is geologically unexplored. This paper brings out for the first time the geology and regional tectonic set-up of the area on the basis of LANDSAT imagery interpretation and field checks. Image analysis of about 3700 sq. km area and field observations along selected sections of the area reveal that the area mainly comprises mica-chlorite schists, quartzites and phyllites i.e. low to medium grade Chail metamorophics with acid and basic intrusions within them.

Various photo recognition elements have assisted in the identification of prevailing geomorphic and broad geologic features and in analysing the hydrogeological characters of the region. The paper also discusses the limitations imposed by air photo-interpretation.

The paper decribes the lineaments in the coastal area of Goa identified on aerial photographs and their correlation with the lineaments reported from the adjoining areas. The majority of lineaments along the coastal part of Goa follows a NW-SE, NE-SW and a few ENE-WSW trends. The NW-SE direction is predominant and corresponds with the regional Dharwarian trend followed by NE-SW trending linears due to cross folding of Dharwar Orogeny.

This paper is based on a case study of Delhi. The attempt has been made to find out the usefulness of aerial remote sensing in the study of urban fringe where all sorts of haphazard developments take place. Unauthorised construction, illegal sub-division of land, urban sprawl, environmental degradation are all parts of urban fringe Attempt has been made to find out the efficacy of aerial photos at pre-planning stage and their role in assigning various landuses for planned development of urban fringe zone. For the first time, the concept of “Urban Index” has been used to determine the scale of urbanity by aerial remote sensing technique. Growth of settlements, land speculation, impact of anticipated development proposal have been studied. Aerial photographs have been used to detect depressions, drainage pattern, flooding problems, location of high ground water table areas which are very important for a comprehensive planning and for environmental protection. Limitations of topomaps compared to aerial photographs have been worked out. The study is based on air photo-interpretation technique supported by field checking. *** DIRECT SUPPORT *** A04KF011 00003

The increasing awareness in computer aided technology for mapping and analysis is advancing in developing countries. There are many systems available in this field which links graphical data and attribute data using relational data base management system to meet the needs of various users. MASMAP/MIMER is one such system of fourth generation for rapid mapping and designing. Taking into account the MASMAP as a tool for the application in management of sewerage network, a case study of Rohini, located in west of Delhi, has been discussed in this paper.

This paper presents a case study of the utility of Landsat MSS imagery for soil resoruces mapping in Silent Valley and its environs covering about 33,000 sq. km. area. A collective approach involving monoscopic visual interpretation of Landsat imagery in conjunction with the lithological and topographical information supported by limited field check has been followed to prepare a soil map on 1:250,000 scale showing sub-groups/association of sub-groups. Future prospect of using spaceborne data for soil mapping has also been discussed.

In this paper an attempt has been made to estimate the growingstock showing high (51% and up), medium (25–50%) and low (below 25%) density areas and to prepare a broad landuse and forest cover type map by analysing black and white band 5 Satellite imagery (Landsat-1) on 1:1 m scale pertaining to Western Nepal area which comprises Karnali, Bheri, Babai and Rapti Catchments covering a geograghical area of about 50,000 sq km. The information regarding location, extent and area falling in forested and non-forested categories arrived at from Landsat maps provide basic information to planners at the time of formulation of inventory design before taking up preinvestment Surveys. In developing countries where vegetation stock maps are not readily available, forest resources map based on analysis of Landsat data can be prepared in less time and at a lower cost. Such maps may be immensely used by planners at regional or national level. The technique merits further investigation.

Visual interpretation of LANDSAT imagery in bands 6 and 7 covering Panna district, Bundelkhand, Madhya Pradesh was carried out for terrain analysis and present land use. The physiographic units viz. hills and hill ranges, escarpments, ridges, table-land, pediment, piedmont plain and sub-units of second order were identified and delineated using inter pretation elements. Area of each unit and sub-unit is calculated and presented in the table showing extent, tone, texture, shape and land use. Land use in relation to physiographic units is also described in the paper and shown in the form of small scale map. Visual interpretation technique helps in broad terrain analysis on scale 1:1 million. This information could be used for making a rapid resource inventory.

The paper highlights the findings of photogeological studies, with selective field checks, carried out in Sainj Valley. For the first time, a photogeological map of the area has been prepared using large scale aerial photographs

In this paper the assessment of hyper-altitude photogrpahs for geological interpretation is discussed. The interpretation of three satellite photographs, one from Zagros mountains, Iran and two from Morocco are discussed in detail. It is found that the near vertical photographs of Zagros mountain and East of Ifni Island, Morocco, provide a tremendous wealth of geological information which can safely be utilized for the preparation of good quality small scale geological maps. The oblique photograph of Morocco covering parts of Atlantic coast south of Agadir provides much less geological information compared to the vertical photos.